Author(s): Shaik Shameem, Lavanya Kumari P, Ramana Murthy B and N Vani

Abstract: Castor (Ricinus communis L.) is an important oilseed crop in Andhra Pradesh, contributing significantly to India's economy through oil exports. Although it has been cultivated for a long time, castor accounts for only a small portion of global vegetable oil production. Its oil is crucial in the chemical industry as the sole commercial source of hydroxylated fatty acids. Accurate forecasting of wholesale castor prices is vital for stakeholders to manage risks effectively. For this purpose, secondary data on castor prices in Andhra Pradesh from 2008-2024 was analyzed using autocorrelation with the Box-Pierce test. Various machine learning models were developed, including ARIMA, ANN, SVR, ELM, hybrids combining these methods and wavelet decomposition models. The ARIMA+ELM model demonstrated the best predictive accuracy, aiding in better price forecasting and contributing to market stability and economic efficiency in agriculture.

DOI: 10.22271/maths.2025.v10.i5b.2037

Pages: 92-98 | Views: 339 | Downloads: 13

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